ML Systems Engineer (Agentic AI & Inference Engineering) (India)

OVERVIEW We are hiring an ML Systems Engineer to design and deliver cutting-edge AI solutions for enterprise clients at the frontier of agentic AI, inference engineering, and ML systems architecture. You will go beyond applied ML - dissecting how AI systems are built, optimized, and scaled - designing production- grade architectures spanning retrieval systems, inference pipelines, and agentic workflows. You will translate state-of-the-art capabilities into robust, performant solutions, operating at the intersection of ML research awareness and engineering discipline. KEY RESPONSIBILITIES - Design and deliver production-grade AI systems for enterprise clients spanning agentic workflows, LLM inference pipelines, and retrieval-augmented architectures. - Lead ML systems architecture decisions - model serving topology, inference backend selection, KV cache management, batching strategies, and memory optimization - alongside ML performance engineering to profile bottlenecks, benchmark throughput/latency, and evaluate quantization strategies (GPTQ, AWQ, GGUF). - Architect RAG pipelines and agentic AI systems - from chunking, embedding, hybrid retrieval, and re- ranking through to multi-agent orchestration, tool use, and memory architectures. - Evaluate frontier model capabilities - reasoning models, multimodal systems, fine-tuned variants - and make principled architectural trade-off decisions for client contexts. - Build reusable accelerators, reference implementations, and evaluation/observability frameworks encoding best practices across engagements. - Contribute to technical solutioning - architecture designs, proof-of-concepts, and feasibility assessments - in client-facing contexts. TECHNICAL QUALIFICATIONS Core Requirements - Python & ML ecosystem: Strong programming skills with production AI system experience; hands- on with the PyTorch ecosystem including Hugging Face Transformers, PEFT, Accelerate, and Datasets. - LLM inference & serving: Deep knowledge of KV cache mechanics, quantization, and batching; hands-on with at least one inference runtime (vLLM, TGI, TensorRT-LLM, SGLang, or similar). - Hands-on experience supporting AI/ML and LLM inference platforms at scale, including working with vLLM for high-performance LLM serving, optimization, and large-scale inference. - RAG & Agentic Systems: Experience designing retrieval architectures and building agentic systems using LangGraph, LlamaIndex Workflows, AutoGen, or CrewAI - including tool use, memory, and multi-agent coordination. - LLM APIs & prompt engineering: Robust grasp of structured output generation, function calling, and provider SDK usage across OpenAI, Anthropic, Mistral, Hugging Face, and similar. - Deployment fundamentals: Proficiency with Docker, containerization, and Linux environments for packaging, deploying, and debugging AI systems. - Comfortable leveraging AI-assisted tools for collaborative development, code generation, refactoring, and productivity enhancement. Preferred - Fine-tuning: Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understanding of when fine-tuning is the right lever vs. prompting or RAG. - Low-level AI systems: Familiarity with CUDA, Triton, or similar GPU programming models; working knowledge of C++ or Rust. - Infrastructure & observability: Kubernetes for containerized AI workloads; experience with LangSmith, Arize, W&B, Phoenix, or Prometheus/Grafana for ML observability. WAYS TO STAND OUT FROM THE CROWD - You have built and deployed a production agentic system and can speak to the failure modes and design decisions that only emerge at runtime. - You have done inference optimization at a systems level - tuning serving infrastructure, implementing custom batching logic, or optimizing a quantization pipeline to hit real SLAs. - You have open-source contributions to prominent ML systems repositories - vLLM, SGLang, llama.cpp, TGI, LangChain, LlamaIndex, or similar - demonstrating work that holds up to community scrutiny. - You have designed custom LLM evaluation frameworks with structured regression harnesses, domain-specific evals, or human-in-the-loop feedback loops - beyond off-the-shelf metrics. - You bring a client-facing engineering mindset and can defend opinions on reasoning models, long- context retrieval, or inference hardware tradeoffs based on hands-on experimentation. Compensation: 500,000.00 - 1,800,000.00 per year Benefits: - Provident Fund Work Location: In person .